Measuring senescence in wild animal populations: towards a longitudinal approach
Why this work is in the frame
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Bibliographic record
Abstract
1 A major current challenge in ageing research is to understand why senescence rates vary between individuals, populations and species in wild populations. 2 Recent studies clearly illustrate that senescent declines in key demographic and life-history traits can be observed in many wild animal systems. 3 Here, we summarize the key challenges facing researchers working to understand senescence in the wild. We concentrate on: (i) limited data availability, (ii) the substantial individual heterogeneity typical of wild populations, (iii) incomplete capture histories, and (iv) trade-offs across the life span. 4 We discuss analytical methods to overcome these challenges. We advocate the use of Capture–Mark–Recapture models to remove likely bias associated with re-sampling rates of less than one. We also illustrate that ageing trajectories may vary between different traits in wild populations. Wherever possible, researchers should examine ageing patterns in multiple traits. 5 Numerous models are available to describe the rate and shape of senescence in free-living populations, but there is currently little consensus regarding which is most appropriate in analyses of wild organisms. 6 We argue that only longitudinal studies of marked or recognizable individuals provide reliable sources of information in the study of senescence. Senescence is a within-individual process and only longitudinal studies allow researchers to separate within-individual ageing patterns from between-individual heterogeneity. 7 We examine two analytical approaches to measure ageing using longitudinal data from wild populations: a jack-knifing approach, well-suited to modelling survival probability, and a mixed-effects model approach. Both methods control for sources of between-individual heterogeneity to allow more accurate measurement of within-individual ageing patterns.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it